Human activity recognition methods are used in several applications such as human-computer interaction, robot learning, and analyzing video surveillance. Although several methods have been proposed for activity recognition, most of them ignore the relation between adjacent video frames and thus they fail to recognize some actions. In this study we propose an unsupervised algorithm to segment the input video into subsequences. Each subsequence contains a part of the main action happening in the video. This algorithm analyzes the temporal coherence of the adjacent frames using several similarity measures. We show preliminary results using two state-of-the-art action recognition datasets, namely HMDM51 and Hollywood2.